Publication | Closed Access
IIT TREC-9 - Entity Based Feedback with Fusion.
12
Citations
9
References
2000
Year
Unknown Venue
Artificial IntelligenceEngineeringIntelligent Information RetrievalIit Trec-9Semantic WebFifty QueriesCorpus LinguisticsText MiningNatural Language ProcessingInformation RetrievalData ScienceRelevance FeedbackFusion LearningData IntegrationQuery ExpansionNamed-entity RecognitionData FusionKnowledge DiscoveryComputer ScienceQuery AnalysisAutomated ReasoningWeb TrackInteractive Information Retrieval
For TREC-9, we focused on effectiveness in the web track. The key techniques we employed were information fusion, entity-based relevance feedback, Wordnet-based query parsing and a user interface designed to assist with web-based manual queries. Our initial results are positive. For the manual task, forty of fifty queries are over the median. In the adhoc, title-only task, thirty-four of fifty queries are over the median.
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